Transgenic Mice as a Model of Pre-Clinical Alzheimers Disease
Bibliographic record
Abstract
At diagnosis, Alzheimer's disease (AD) brains are extensively burdened with plaques and tangles and display a degree of synaptic failure most likely beyond therapeutic treatment. It is therefore crucial to identify early pathological events in the progression of the disease. While it is not currently feasible to identify and study early, pre-clinical stages of AD, transgenic (Tg) models offer a valuable tool in this regard. Here we investigated cognitive, structural and biochemical CNS alterations occurring in our newly developed McGill-Thyl-APP Tg mice (over-expressing the human amyloid precursor protein with the Swedish and Indiana mutations) prior to extracellular plaque deposition. Pre-plaque, 3-month old Tg mice already displayed cognitive deficits concomitant with reorganization of cortical cholinergic pre-synaptic terminals. Conformational specific antibodies revealed the early appearance of intracellular amyloid β (Aβ)-oligomers and fibrillar oligomers in pyramidal neurons of cerebral cortex and hippocampus. At the same age, the cortical levels of insulin degrading enzyme -a well established Aβ-peptidase, were found to be significantly down-regulated. Our results suggest that, in the McGill-Thy1-APP Tg model, functional, structural and biochemical alterations are already present in the CNS at early, pre-plaque stages of the pathology. Accumulation of intraneuronal neurotoxic Aβ-oligomers (possibly caused by a failure in the clearance machinery) is likely to be the culprit of such early, pre-plaque pathology. Similar neuronal alterations might occur prior to clinical diagnosis in AD, during a yet undefined 'latent' stage. A better understanding of such pre-clinical AD might yield novel therapeutic targets and or diagnostic tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".